Dr. Lee Gross’s Epiphany Healthcare provides DPC services for some of the employees and some members of of some of their families at a hospital in Florida. Some hospital employees decline Epiphany; they and some members of their families receive instead traditional insurance based primary care. Unusually for such arrangements, a recent assessment of the dollar value of the claims experience for the DPC cohort exceeded that of the FFS component by about 15%.
There appears to have never been a case in which a DPC provider applied risk-based adjustment to validate raw claims cost differences that seemed to reflect well on DPC. When, on the other hand, raw data went the opposite way for him, Dr. Gross decided the time had come to became a data analytics pioneer and develop a risk adjustment model for direct primary care.
Conveniently, widely-used models developed by professional actuaries in and for CMS (in making patient population risk adjustments for use in Medicare Advantage plans and under the Affordable Care Act) and Dr Gross’s share a common feature – each adds up, in certain circumstances, the total number of a patient’s chronic conditions to develop an adjustment factor. This facilitates a comparison.
To assess the precise role of the common feature of the two models, respectively, I will summarize the more widely used CMS model and then explain some key differences between it and the Gross model.
In CMS’s method, depending on the case, up to four different components come into play. [FYI, the lettering system is mine and is intended to simplify the explanation.]
A. There is, of course, a significant demographics component scored to reflect the historical utlilization by persons, specified by ages, gender, and other factors. Basic demographics are at the core of risk adjustments used by CMS for the ACA; over 75% of ACA individual enrollees under 65 have no adjustment-worthy chronic conditions and are risk-adjusted on demographics alone.
B. There is a specific conditions component in which a patient is given a score for each condition she has off a list of over eighty broad health conditions, each of which has a specific score reflecting that conditions historical correlation with the use of medical services – so much for asthma, so much for porphyria, CHF, etc.
C. There is also an “interactions” component in which the patients can receive additional scores for as many as she may have off a list for certain specific combinations of conditions from “B” above that are historically correlated with enhanced need for services when the those health conditions are combined.
D. a recently added number of chronic conditions component reflects considerations generally similar to those in Dr Gross’s index
(D)(1) there is a specific list of about two dozen chronic conditions of the eighty condition from “B” above
(D)(2) if a patient has FOUR or more of the conditions in the list in (D)(1), then patient is given an additional score that reflects how having that many of those conditions has historically been correlated with an enhanced need for services.
Component (D) scoring is non-linear: the adjustment for five conditions reflects historical correlation between costs and having five conditions; scoring is computed separately for four, five, six, etc. Eight conditions are not simply given twice the score given for four conditions.
Now let’s look under the hood of the Gross model to see how these factors play out in his risk adjustment model for direct primary care.
Gross’s model removes the demographic component.
Under Gross’s model the number of different specific conditions being scored is reduced from a hierarchy of over eighty condition categories down to a single data point: a “chronic condition”.
Gross’s model removes the interactions component.
Gross’s model counts all chronic conditions equally and assumes a linear relationship between health costs and the raw number of chronic conditions.
Gross’s model starts the count of multiple chronic conditions needed to trigger the multiple chronic conditions factor at one (1), while the CMS model believes that number of conditions requiring a complexity factor should start at four (4).
A theoretical advantage of Gross’s method is ease of application.
On the other side, Gross’s is untethered to any historical utilization data whatsoever. Within the one component where there is any measure of similarity between the Gross model and the CMS model, Gross’s approach expressly contradicts CMS’s actuaries’ explicit determinations that a linear correlation did not accord with historical reality and that the count should begin at four (4). (CMS actuaries did agree that there was an additive effect for each additional condition, but unlike Gross they denied that the additive effect was necessarily linear.)
On every other component Gross’s model is in even sharper contrast to the views of the professionals. Notwithstanding Dr Gross’s model, demographics strongly correlate with costs. As noted, over 75% of patients under 65 have their risk adjustments based only on demographics as they have no chronic conditions among the many dozens that the professionals felt needed to be includes. Notwithstanding Dr Gross’s model, among the 25 or less with actionable chronic conditions, costs vary profoundly from one condition to another; conditions are hierarchical. Notwithstanding Dr Gross’s model, multiple conditions do interact with each other.
Gross’s risk adjustment model for direct primary care looks like something that has been reverse engineered from adverse data for the sole purpose of rescuing Dr Gross’s corporate brand at its largest employer.
As with all other aspects of his otherwise relentless promotion of the results at Desoto Memorial Hospital, Dr Gross has expressly declined to make public any details of his methods of gathering and processing data. Even if the very outline of his risk methodology was not at war with those of his analytical betters, important questions would remain.
For example, what criteria did he use to identify and count chronic conditions in the DPC cohort (his own patients) and those in the non-DPC patient cohort (who get primary care elsewhere). Especially for gathering chronic condition data on those who are not his patients what, if anything, did he do to validate data accuracy and consistency?
For these reasons, I report only this: (See update below.)
The raw data shows that non-Epiphany patients at Epiphany’s largest employer clinic have lower claims costs.
Dr Gross has produced a slide show that includes, as far as I can tell, all the public information about the Epiphany experience at that employer.
The slide show utilizes the Gross risk adjustment methodology for direct primary care discussed above.
On Slide 22, Dr Gross made an error of double adjustment. Here is what appears.
|Chronic Conditions||846 per 1,000||623 per 1,000|
|Relative Chronic Conditions||1.35||0.74|
There is at least one other, similar error.
Actually, I have decided to go step further by announcing an opinion: I think Dr Gross’s approach is complete bullshit. And yet it still may be a bit more justifiable than the bullshit of the KPI Ninja/Nextera report; Gross, after all, is not falsely claiming the imprimatur of a prestigious academic research team.